About BookGenie

BookGenie is your AI-powered companion for discovering books you'll love.

Designed as a personalized book discovery engine, BookGenie uses data-driven techniques like popularity-based filtering and collaborative filtering to recommend titles that resonate with individual preferences.

Why BookGenie?

  • Hybrid model: Popularity + Collaborative Filtering
  • Discover trending books and reader favorites
  • Built with clean UI, user feedback, and error handling

Technologies Used

  • Python, Flask, Pandas, NumPy, Jinja2
  • Popularity Ranking, Collaborative Filtering
  • Bootstrap 5, HTML, CSS

My Development Journey

BookGenie started as a hands-on project to practically apply what I’d learned about recommendation systems and full-stack development. I began by cleaning and preprocessing a real-world book ratings dataset sourced from Kaggle, removing duplicates and nulls, and curating it for meaningful input.

To surface highly rated and well-reviewed books, I implemented a popularity-based ranking system using an IMDb-style weighted average. For personalized suggestions, I developed a collaborative filtering model based on user-book interaction similarity, enabling recommendations for users based on the behavior of similar readers.

I handled the backend using Flask, managing routes, user input, and recommendation logic. The UI was crafted with Bootstrap 5 and custom CSS, prioritizing clarity, responsiveness, and real-app structure. Features like error handling, flash messages, and modular templates were added to make the app robust and user-friendly.

Every feature from trending books to recommendation forms was built and validated end-to-end, demonstrating my ability to work across the stack and think in terms of product quality, not just technical correctness.

BookGenie reflects my practical understanding of recommender systems and my ability to build maintainable, user-focused web applications from scratch.